DATA 698 : Capstone Research Project
Capstone Project on CPI and Employment
DATA 698 : Capstone Research Project
1 Overview
We are in totally different era of twenty first century, and it gives us very rare situation where any positive news would help the humankind. We want to use the historical CPI data and find the relation of it with the employment, in hope that we would have some positive news on employment by following the trend of the data in past.
We feel that lower CPI would result in more job opportunity, as the it gives space for more competition in small business across sectors.
2 Capstone Project on CPI and Employment
- The estimates of employment for 1998-2006 are based on the 2002 North American Industry Classification System (NAICS). The estimates for 2007-2010 are based on the 2007 NAICS. The estimates for 2011-2016 are based on the 2012 NAICS. The estimates for 2017 forward are based on the 2017 NAICS.
- Excludes limited partners.
- Under the 2007 NAICS, internet publishing and broadcasting was reclassified to other information services.
- Not shown to avoid disclosure of confidential information; estimates are included in higher-level totals.
- Estimate for employment suppressed to cover corresponding estimate for earnings. Estimates for this item are included in the total.
- (NA) Not available.
- (NM) Not meaningful.
Last updated: September 24, 2019- new statistics for 2018; revised statistics for 2014-2017.
3 Data Preparation
Load the required libraries
#packages <- c("R.rsp","pandocfilters","knitr","plyr","tidyr","dplyr","ggplot2","plotly","sqldf","MASS","reshape2","Amelia","mice","googleVis","stringi","ROCR","scatterplot3d","visdat","gridExtra","sjmisc")
#install.packages (packages, repos="http://cran.us.r-project.org", dependencies=TRUE, lib="C:/Program Files/R/R-3.6.3/library")
#invisible (lapply (packages, library, character.only = T))
#install.packages("devtools", lib="C:/Program Files/R/R-3.6.3/library")
#devtools::install_github("hadley/ggplot2")
library(tidyverse)
library(kableExtra)
library(lubridate)
library(forecast)
library(stringi)
library(dplyr)3.1 Load Employment Datasets
file_path <- "../data/cpi/SAINC/"
EMP_DATA_FILE <- paste0(file_path,"EMP9818.csv")
EMP_DATA <- read_delim(EMP_DATA_FILE, delim = ",", col_names = TRUE, trim_ws= TRUE)
#,skip = 6,col_types = list(col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character(),col_character()))
# names(SAP_INV_DAT) = c("DROP","I_BILLDATE","I_ORG","I_DC","I_DOCCA","I_BILLTYPE","I_ORDER_REA","I_DIV","I_MATYPE","I_BILL_QTY","BLANK")
# SAP_INV_DAT<- SAP_INV_DAT[-which(is.na(SAP_INV_DAT$I_ORG)),] %>% .[-str_which(trimws(.$I_ORG),'SOrg.|-----'),c(-1,-11)]
# 3
# SAP_INV_DAT$I_BILLDATE <- date(parse_datetime(SAP_INV_DAT$I_BILLDATE, "%m/%d/%Y"))
#
# head(SAP_INV_DAT)3.2 Show Relevant Employment Data
## GeoFIPS GeoName Region TableName
## Length:7084 Length:7084 Min. :1.000 Length:7084
## Class :character Class :character 1st Qu.:3.000 Class :character
## Mode :character Mode :character Median :5.000 Mode :character
## Mean :4.475
## 3rd Qu.:6.000
## Max. :8.000
## NA's :122
## LineCode IndustryClassification Description Unit
## Min. : 10.0 Length:7084 Length:7084 Length:7084
## 1st Qu.: 517.0 Class :character Class :character Class :character
## Median : 712.5 Mode :character Mode :character Mode :character
## Mean : 872.1
## 3rd Qu.:1103.0
## Max. :2012.0
## NA's :4
## 1998 1999 2000 2001
## Length:7084 Length:7084 Length:7084 Length:7084
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## 2002 2003 2004 2005
## Length:7084 Length:7084 Length:7084 Length:7084
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## 2006 2007 2008 2009
## Length:7084 Length:7084 Length:7084 Length:7084
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## 2010 2011 2012 2013
## Length:7084 Length:7084 Length:7084 Length:7084
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## 2014 2015 2016 2017
## Length:7084 Length:7084 Length:7084 Length:7084
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## 2018
## Length:7084
## Class :character
## Mode :character
##
##
##
## ## Observations: 5,760
## Variables: 24
## $ LineCode <dbl> 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, 101, ...
## $ Description <chr> "Forestry and logging", "Forestry and logging", "Forest...
## $ GeoName <chr> "United States", "Alabama", "Alaska", "Arizona", "Arkan...
## $ `1998` <chr> "168100", "10193", "1397", "598", "7248", "7200", "698"...
## $ `1999` <chr> "168700", "9637", "1455", "(T)", "7266", "7273", "727",...
## $ `2000` <chr> "153300", "8852", "1307", "(T)", "6662", "6208", "693",...
## $ `2001` <chr> "148400", "(D)", "986", "(D)", "6087", "6139", "637", "...
## $ `2002` <chr> "144600", "8347", "858", "(D)", "(D)", "5917", "(D)", "...
## $ `2003` <chr> "133800", "7929", "800", "(D)", "5716", "5340", "(D)", ...
## $ `2004` <chr> "140100", "8222", "(D)", "363", "(D)", "5545", "(D)", "...
## $ `2005` <chr> "147300", "8187", "790", "(D)", "5949", "5776", "(D)", ...
## $ `2006` <chr> "142700", "8124", "623", "(D)", "(D)", "5610", "(D)", "...
## $ `2007` <chr> "138100", "7800", "520", "(D)", "(D)", "5656", "(D)", "...
## $ `2008` <chr> "136900", "7850", "460", "(D)", "5095", "5861", "(D)", ...
## $ `2009` <chr> "119800", "7056", "440", "318", "4408", "4841", "(D)", ...
## $ `2010` <chr> "111600", "6741", "(D)", "(D)", "4160", "4626", "579", ...
## $ `2011` <chr> "116800", "6962", "(D)", "298", "4229", "4991", "558", ...
## $ `2012` <chr> "134600", "7728", "505", "(D)", "(D)", "5603", "726", "...
## $ `2013` <chr> "138000", "7970", "501", "388", "(D)", "5566", "755", "...
## $ `2014` <chr> "133000", "7843", "525", "(D)", "(D)", "5256", "699", "...
## $ `2015` <chr> "141300", "8059", "534", "(D)", "(D)", "5564", "(D)", "...
## $ `2016` <chr> "138800", "7892", "503", "(D)", "4890", "5266", "795", ...
## $ `2017` <chr> "123900", "7107", "(D)", "(D)", "4405", "4975", "691", ...
## $ `2018` <chr> "117600", "6759", "401", "379", "4248", "4796", "641", ...## Observations: 118
## Variables: 3
## $ LineCode <fct> 10, 20, 40, 50, 60, 70, 80, 90, 100, 101, 102, 103, 200...
## $ Description <fct> "Total employment (number of jobs)", "Wage and salary e...
## $ NA <fct> "A count of jobs, both full-time and part-time. It incl...3.3 Load CPI Data
file_path <- "../data/cpi/archive/"
## Read files named *.csv
filenames <- list.files(path=file_path,
pattern="*.csv",
full.names = TRUE)
#print(filenames)
##Create list of data frame names without the ".csv" part
names <- gsub(pattern='\\.',
replacement='_', # remove the last 4 characters from filename (_clean)
x=(sub(pattern = "(.*)\\..*$", replacement = "\\1", # remove extenson from filename (.csv)
basename(filenames) # get filename without the directory file path
)
)
)
#print(names)
###Load all files
for(i in filenames){
## Create list of data frame names without the ".csv" part
name <- gsub(pattern='\\.',
replacement='_', # remove the last 4 characters from filename (_clean)
x=(sub(pattern = "(.*)\\..*$", replacement = "\\1", # remove extenson from filename (.csv)
basename(i) # get filename without the directory file path
)
)
)
print(name)
assign(x=name, value=data.frame(read.csv(i)))
#DT::datatable(name)
}## [1] "cu_area"
## [1] "cu_base"
## [1] "cu_data_0_Current"
## [1] "cu_data_1_AllItems"
## [1] "cu_data_10_OtherWest"
## [1] "cu_data_11_USFoodBeverage"
## [1] "cu_data_12_USHousing"
## [1] "cu_data_13_USApparel"
## [1] "cu_data_14_USTransportation"
## [1] "cu_data_15_USMedical"
## [1] "cu_data_16_USRecreation"
## [1] "cu_data_17_USEducationAndCommunication"
## [1] "cu_data_18_USOtherGoodsAndServices"
## [1] "cu_data_19_PopulationSize"
## [1] "cu_data_2_Summaries"
## [1] "cu_data_20_USCommoditiesServicesSpecial"
## [1] "cu_data_3_AsizeNorthEast"
## [1] "cu_data_4_AsizeNorthCentral"
## [1] "cu_data_5_AsizeSouth"
## [1] "cu_data_6_AsizeWest"
## [1] "cu_data_7_OtherNorthEast"
## [1] "cu_data_8_OtherNorthCentral"
## [1] "cu_data_9_OtherSouth"
## [1] "cu_footnote"
## [1] "cu_item"
## [1] "cu_period"
## [1] "cu_periodicity"
## [1] "cu_series"3.4 Show CPI Reference Data
3.4.1 Area
3.4.2 Base
3.4.3 Item
3.4.4 Periodicity
3.4.5 Series
3.4.6 Period
## [1] All items - old base
## [2] Purchasing power of the consumer dollar - old base
## [3] All items
## [4] Energy
## [5] All items less food
## [6] All items less food and shelter
## [7] All items less food and energy
## [8] All items less shelter
## [9] All items less medical care
## [10] All items less energy
## [11] Purchasing power of the consumer dollar
## [12] Apparel less footwear
## [13] Apparel
## [14] Men's and boys' apparel
## [15] Women's and girls' apparel
## [16] Commodities
## [17] Energy commodities
## [18] Commodities less food
## [19] Commodities less food and beverages
## [20] Commodities less food and energy commodities
## [21] Durables
## [22] Education and communication
## [23] Education
## [24] Communication
## [25] Information and information processing
## [26] Education and communication commodities
## [27] Education and communication services
## [28] Food and beverages
## [29] Food
## [30] Food at home
## [31] Cereals and bakery products
## [32] Meats
## [33] Fruits and vegetables
## [34] Fresh fruits and vegetables
## [35] Nonalcoholic beverages and beverage materials
## [36] Other food at home
## [37] Alcoholic beverages
## [38] Other goods and services
## [39] Personal care
## [40] Other goods
## [41] Other personal services
## [42] Housing
## [43] Shelter
## [44] Fuels and utilities
## [45] Household energy
## [46] Household furnishings and operations
## [47] Household furnishings and supplies
## [48] Medical care
## [49] Medical care commodities
## [50] Medical care services
## [51] Nondurables
## [52] Domestically produced farm food
## [53] Nondurables less food
## [54] Nondurables less food and beverages
## [55] Nondurables less food and apparel
## [56] Recreation
## [57] Recreation commodities
## [58] Recreation services
## [59] Services
## [60] Utilities and public transportation
## [61] Rent of shelter
## [62] Other services
## [63] Transportation services
## [64] Services less rent of shelter
## [65] Services less medical care services
## [66] Services less energy services
## [67] Transportation
## [68] Private transportation
## [69] Transportation commodities less motor fuel
## [70] Men's apparel
## [71] Men's suits
## [72] Men's furnishings
## [73] Men's shirts and sweaters
## [74] Men's pants and shorts
## [75] Boys' apparel
## [76] Women's apparel
## [77] Women's outerwear
## [78] Women's dresses
## [79] Women's suits and separates
## [80] Women's underwear
## [81] Girls' apparel
## [82] Footwear
## [83] Men's footwear
## [84] Boys' and girls' footwear
## [85] Women's footwear
## [86] Infants' and toddlers' apparel
## [87] Jewelry and watches
## [88] Watches
## [89] Jewelry
## [90] Educational books and supplies
## [91] Tuition
## [92] College tuition and fees
## [93] Elementary and high school tuition and fees
## [94] Child care and nursery school
## [95] Technical and business school tuition and fees
## [96] Postage and delivery services
## [97] Postage
## [98] Delivery services
## [99] Telephone services
## [100] Wireless telephone services
## [101] Land-line telephone services
## [102] Information technology
## [103] Personal computers and peripheral equipment
## [104] Computer software and accessories
## [105] Internet services and electronic information providers
## [106] Telephone hardware
## [107] Information technology commodities
## [108] Cereals and cereal products
## [109] Flour and prepared flour mixes
## [110] Breakfast cereal
## [111] Rice
## [112] Bakery products
## [113] Bread
## [114] Fresh biscuits
## [115] Cakes
## [116] Other bakery products
## [117] Beef and veal
## [118] Uncooked ground beef
## [119] Uncooked beef roasts
## [120] Uncooked beef steaks
## [121] Uncooked other beef and veal
## [122] Pork
## [123] Bacon
## [124] Ham
## [125] Pork chops
## [126] Other pork including roasts and picnics
## [127] Other meats
## [128] Poultry
## [129] Chicken
## [130] Other poultry including turkey
## [131] Fish and seafood
## [132] Fresh fish and seafood
## [133] Processed fish and seafood
## [134] Eggs
## [135] Dairy and related products
## [136] Milk
## [137] Cheese and related products
## [138] Ice cream and related products
## [139] Other dairy and related products
## [140] Fresh fruits
## [141] Apples
## [142] Bananas
## [143] Citrus fruits
## [144] Other fresh fruits
## [145] Fresh vegetables
## [146] Potatoes
## [147] Lettuce
## [148] Tomatoes
## [149] Other fresh vegetables
## [150] Processed fruits and vegetables
## [151] Canned fruits and vegetables
## [152] Frozen fruits and vegetables
## [153] Other processed fruits and vegetables including dried
## [154] Juices and nonalcoholic drinks
## [155] Carbonated drinks
## [156] Frozen noncarbonated juices and drinks
## [157] Nonfrozen noncarbonated juices and drinks
## [158] Beverage materials including coffee and tea
## [159] Coffee
## [160] Other beverage materials including tea
## [161] Sugar and sweets
## [162] Sugar and artificial sweeteners
## [163] Candy and chewing gum
## [164] Other sweets
## [165] Fats and oils
## [166] Butter and margarine
## [167] Salad dressing
## [168] Other fats and oils including peanut butter
## [169] Other foods
## [170] Soups
## [171] Frozen and freeze dried prepared foods
## [172] Snacks
## [173] Spices
## [174] Baby food
## [175] Other miscellaneous foods
## [176] Food away from home
## [177] Full service meals and snacks
## [178] Limited service meals and snacks
## [179] Food at employee sites and schools
## [180] Food from vending machines and mobile vendors
## [181] Other food away from home
## [182] Alcoholic beverages at home
## [183] Beer
## [184] Distilled spirits at home
## [185] Wine at home
## [186] Alcoholic beverages away from home
## [187] Tobacco and smoking products
## [188] Cigarettes
## [189] Tobacco products other than cigarettes
## [190] Personal care products
## [191] Hair
## [192] Cosmetics
## [193] Personal care services
## [194] Haircuts and other personal care services
## [195] Miscellaneous personal services
## [196] Legal services
## [197] Funeral expenses
## [198] Laundry and dry cleaning services
## [199] Apparel services other than laundry and dry cleaning
## [200] Financial services
## [201] Miscellaneous personal goods
## [202] Rent of primary residence
## [203] Lodging away from home
## [204] Housing at school
## [205] Other lodging away from home including hotels and motels
## [206] Owners' equivalent rent of residences
## [207] Owners' equivalent rent of primary residence
## [208] Tenants' and household insurance
## [209] Fuel oil and other fuels
## [210] Fuel oil
## [211] Propane
## [212] Energy services
## [213] Electricity
## [214] Utility (piped) gas service
## [215] Water and sewer and trash collection services
## [216] Water and sewerage maintenance
## [217] Garbage and trash collection
## [218] Window and floor coverings and other linens
## [219] Floor coverings
## [220] Window coverings
## [221] Other linens
## [222] Furniture and bedding
## [223] Bedroom furniture
## [224] Living room
## [225] Other furniture
## [226] Appliances
## [227] Major appliances
## [228] Other appliances
## [229] Other household equipment and furnishings
## [230] Clocks
## [231] Indoor plants and flowers
## [232] Dishes and flatware
## [233] Nonelectric cookware and tableware
## [234] Tools
## [235] Outdoor equipment and supplies
## [236] Housekeeping supplies
## [237] Household cleaning products
## [238] Household paper products
## [239] Miscellaneous household products
## [240] Household operations
## [241] Domestic services
## [242] Gardening and lawncare services
## [243] Moving
## [244] Repair of household items
## [245] Professional services
## [246] Physicians' services
## [247] Dental services
## [248] Eyeglasses and eye care
## [249] Services by other medical professionals
## [250] Hospital and related services
## [251] Hospital services
## [252] Nursing homes and adult day services
## [253] Care of invalids and elderly at home
## [254] Health insurance
## [255] Medicinal drugs
## [256] Prescription drugs
## [257] Nonprescription drugs
## [258] Medical equipment and supplies
## [259] Video and audio
## [260] Televisions
## [261] Cable and satellite television service
## [262] Other video equipment
## [263] Video discs and other media
## [264] Audio equipment
## [265] Recorded music and music subscriptions
## [266] Video and audio products
## [267] Video and audio services
## [268] Pets
## [269] Pets and pet products
## [270] Pet services including veterinary
## [271] Sporting goods
## [272] Sports vehicles including bicycles
## [273] Sports equipment
## [274] Photography
## [275] Photographic equipment and supplies
## [276] Photographers and film processing
## [277] Other recreational goods
## [278] Toys
## [279] Sewing machines
## [280] Music instruments and accessories
## [281] Other recreation services
## [282] Club membership for shopping clubs
## [283] Admissions
## [284] Fees for lessons or instructions
## [285] Recreational reading materials
## [286] Newspapers and magazines
## [287] Recreational books
## [288] New and used motor vehicles
## [289] New vehicles
## [290] Used cars and trucks
## [291] Leased cars and trucks
## [292] Car and truck rental
## [293] Motor fuel
## [294] Gasoline (all types)
## [295] Other motor fuels
## [296] Motor vehicle parts and equipment
## [297] Tires
## [298] Vehicle accessories other than tires
## [299] Motor vehicle maintenance and repair
## [300] Motor vehicle body work
## [301] Motor vehicle maintenance and servicing
## [302] Motor vehicle repair
## [303] Motor vehicle insurance
## [304] Motor vehicle fees
## [305] State motor vehicle registration and license fees
## [306] Parking and other fees
## [307] Public transportation
## [308] Airline fares
## [309] Other intercity transportation
## [310] Intracity transportation
## [311] White bread
## [312] Bread other than white
## [313] Fresh cakes and cupcakes
## [314] Cookies
## [315] Fresh sweetrolls
## [316] Crackers
## [317] Frozen and refrigerated bakery products
## [318] Bacon and related products
## [319] Breakfast sausage and related products
## [320] Frankfurters
## [321] Lamb and organ meats
## [322] Lamb and mutton
## [323] Lunchmeats
## [324] Fresh whole chicken
## [325] Fresh and frozen chicken parts
## [326] Shelf stable fish and seafood
## [327] Frozen fish and seafood
## [328] Fresh whole milk
## [329] Fresh milk other than whole
## [330] Butter
## [331] Oranges
## [332] Canned fruits
## [333] Frozen vegetables
## [334] Canned vegetables
## [335] Dried beans
## [336] Margarine
## [337] Peanut butter
## [338] Roasted coffee
## [339] Instant coffee
## [340] Salt and other seasonings and spices
## [341] Olives
## [342] Sauces and gravies
## [343] Other condiments
## [344] Prepared salads
## [345] Whiskey at home
## [346] Distilled spirits
## [347] Wine away from home
## [348] Distilled spirits away from home
## [349] Land-line interstate toll calls
## [350] Land-line intrastate toll calls
## [351] Laundry equipment
## [352] Video game hardware
## [353] Stationery
## [354] New cars
## [355] New cars and trucks
## [356] New trucks
## [357] New motorcycles
## [358] Gasoline
## [359] Motor oil
## [360] Vehicle parts and equipment other than tires
## [361] Parking fees and tolls
## [362] Intercity bus fare
## [363] Intercity train fare
## [364] Ship fare
## [365] Intracity mass transit
## [366] Inpatient hospital services
## [367] Outpatient hospital services
## [368] Film and photographic supplies
## [369] Photographic equipment
## [370] Pet food
## [371] Purchase of pets
## [372] Automobile service clubs
## [373] Admission to movies
## [374] Admission to sporting events
## [375] Photographer fees
## [376] Film processing
## [377] Pet services
## [378] Veterinarian services
## [379] Rental of video discs and other media
## [380] Checking account and other bank services
## [381] Tax return preparation and other accounting fees
## [382] College textbooks
## [383] Food at elementary and secondary schools
## [384] Infants' equipment
## [385] Infants' furniture
## 385 Levels: Admission to movies Admission to sporting events ... Women's underwear3.5 Combined Reference Data
3.6 Show CPI Data from Select Sectors
3.6.1 CPI - US Food Beverages
FoodItemCode <- cu_reference_data %>%
filter(grepl('SAF|SEF|SS07', item_code))
#dplyr::filter(item_code %like% 'SAH' | item_code %like% 'SEH')
#DT::datatable((HousingItemCode))
cu_data_USFoodBeverage = cu_data_0_Current %>%
inner_join(FoodItemCode, by="series_id") %>%
inner_join(cu_period, by="period") %>%
filter(year %in% (1998:2018)) %>%
select(c("series_id",
"series_title",
"year",
"period",
"period_abbr",
"period_name",
"value",
"area_code",
"area_name",
"area_display_level",
"area_selectable",
"area_sort_sequence",
"item_code",
"item_name",
"item_display_level",
"item_selectable",
"item_sort_sequence",
"seasonal",
"periodicity_code",
"periodicity_name",
"base_code",
"base_name",
"base_period"))
DT::datatable(head(cu_data_USFoodBeverage))3.6.2 CPI - US Housing
HousingItemCode <- cu_reference_data %>%
#filter(grepl('SAH|SEH', item_code, fixed = TRUE))
filter(grepl('SAH|SEH', item_code))
#dplyr::filter(item_code %like% 'SAH' | item_code %like% 'SEH')
#DT::datatable((HousingItemCode))
cu_data_USHousing = cu_data_0_Current %>%
inner_join(HousingItemCode, by="series_id") %>%
inner_join(cu_period, by="period") %>%
#filter(item_code %like% c('SAH','SEH')) %>%
filter(year %in% (1998:2018)) %>%
select(c("series_id",
"series_title",
"year",
"period",
"period_abbr",
"period_name",
"value",
"area_code",
"area_name",
"area_display_level",
"area_selectable",
"area_sort_sequence",
"item_code",
"item_name",
"item_display_level",
"item_selectable",
"item_sort_sequence",
"seasonal",
"periodicity_code",
"periodicity_name",
"base_code",
"base_name",
"base_period"))
DT::datatable(head(cu_data_USHousing))3.6.3 CPI - US Transportation
TransportItemCode <- cu_reference_data %>%
filter(grepl('SAS24|SAS4|SAT|SETG|SS53', item_code))
#dplyr::filter(item_code %like% 'SAH' | item_code %like% 'SEH')
#DT::datatable((HousingItemCode))
cu_data_USTransportation = cu_data_0_Current %>%
inner_join(TransportItemCode, by="series_id") %>%
inner_join(cu_period, by="period") %>%
filter(year %in% (1998:2018)) %>%
select(c("series_id",
"series_title",
"year",
"period",
"period_abbr",
"period_name",
"value",
"area_code",
"area_name",
"area_display_level",
"area_selectable",
"area_sort_sequence",
"item_code",
"item_name",
"item_display_level",
"item_selectable",
"item_sort_sequence",
"seasonal",
"periodicity_code",
"periodicity_name",
"base_code",
"base_name",
"base_period"))
DT::datatable(head(cu_data_USTransportation))3.6.4 CPI - US Medical
MedicalItemCode <- cu_reference_data %>%
filter(grepl('SAM|SEM', item_code))
#dplyr::filter(item_code %like% 'SAH' | item_code %like% 'SEH')
#DT::datatable((HousingItemCode))
cu_data_USMedical = cu_data_0_Current %>%
inner_join(MedicalItemCode, by="series_id") %>%
inner_join(cu_period, by="period") %>%
filter(year %in% (1998:2018)) %>%
select(c("series_id",
"series_title",
"year",
"period",
"period_abbr",
"period_name",
"value",
"area_code",
"area_name",
"area_display_level",
"area_selectable",
"area_sort_sequence",
"item_code",
"item_name",
"item_display_level",
"item_selectable",
"item_sort_sequence",
"seasonal",
"periodicity_code",
"periodicity_name",
"base_code",
"base_name",
"base_period"))
DT::datatable(head(cu_data_USMedical))3.6.5 CPI - US Education And Communication
EducationItemCode <- cu_reference_data %>%
filter(grepl('SAE|SEEA|SEEB', item_code))
#dplyr::filter(item_code %like% 'SAH' | item_code %like% 'SEH')
#DT::datatable((HousingItemCode))
cu_data_USEducationAndCommunication = cu_data_0_Current %>%
inner_join(EducationItemCode, by="series_id") %>%
inner_join(cu_period, by="period") %>%
filter(year %in% (1998:2018)) %>%
select(c("series_id",
"series_title",
"year",
"period",
"period_abbr",
"period_name",
"value",
"area_code",
"area_name",
"area_display_level",
"area_selectable",
"area_sort_sequence",
"item_code",
"item_name",
"item_display_level",
"item_selectable",
"item_sort_sequence",
"seasonal",
"periodicity_code",
"periodicity_name",
"base_code",
"base_name",
"base_period"))
DT::datatable(head(cu_data_USEducationAndCommunication))